---
language:
- en
license: other
library_name: transformers
tags:
- code
- chemistry
- medical
datasets:
- Locutusque/hyperion-v3.0
widget:
- example_title: Derivative
  text: |
    <|im_start|>user

    In this task, you are given a description of an object and its surroundings. Your
    task is to describe the motion of the object mentioned in the description using
    differential equations. The answer should be a mathematical expression in t. Description:
    A ball is thrown up at 30 ft/sec from 3 feet off the ground. Solve this step-by-step,
    explaining each step in detail.<|im_end|>

    <|im_start|>assistant
  output:
    text: |
      Let's denote the height of the ball as h(t) where t represents time in seconds. Since the ball is thrown up with an initial velocity of 30 ft/sec, we can write the following equation for the vertical component of the ball's velocity:

      v(t) = -32t + 30
      
      Here, v(t) represents the velocity of the ball at time t (in feet per second), and the negative sign indicates that the velocity decreases due to gravity. Now, we can integrate the velocity function to find the position function:
      
      h(t) = ∫v(t) dt = ∫(-32t + 30) dt
      
      Integrating each term separately, we get:
      
      h(t) = (-16t^2 + 30t + C)
      
      To determine the constant C, we use the initial condition that the ball is thrown from 3 feet above the ground. At t=0, the height is 3 feet:
      
      h(0) = 3
      
      Substituting t=0 into the equation for h(t):
      
      3 = (-16(0)^2 + 30(0) + C)
      
      Solving for C, we get:
      
      C = 3
      
      So the final equation for the height of the ball as a function of time is:
      
      h(t) = -16t^2 + 30t + 3
---
# Locutusque/Hyperion-3.0-Yi-34B

![Hyperion](https://th.bing.com/th/id/OIG4.16hfui67GAonzb98DGFF?pid=ImgGn)

## Model Details
- **Model Name**: Locutusque/Hyperion-3.0-Yi-34B
- **Base Model**: Yi-34B  
- **Publisher**: Locutusque  
- **Model Type**: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.  
- **Language**: Multi-domain, English language.  
- **License**: Apache-2.0  

## Model Description
Locutusque/Hyperion-3.0-Yi-34B is a state-of-the-art language model fine-tuned on the Hyperion-v3.0 dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning. This model is designed to greatly outperform its predecessors.

## Intended Use
This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:
- AI-driven tutoring systems for science, medicine, mathematics, and computer science.
- Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
- Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
- Automation in code generation and understanding complex programming context.

## Training Data
The Locutusque/Hyperion-3.0-Yi-34B model was fine-tuned on 150,000 examples of the Hyperion-3.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.

## Quants

ExLlamaV2: https://huggingface.co/bartowski/Hyperion-3.0-Yi-34B-exl2

GGUF: https://huggingface.co/bartowski/Hyperion-3.0-Yi-34B-GGUF

## Evaluation Results
Coming soon

## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Locutusque/Hyperion-3.0-Yi-34B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# For a text generation task
input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate a response
outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Known Limitations

The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.

This model is also very compliant, it will respond to any request. Please make sure to build upon this model with DPO if you plan on using it for enterprise-level deployment.

## Licensing Information

This model is released under the Yi NC license.